Valentin Cousin

🧑‍💻 Full-Stack Data Scientist

I drove end-to-end data science and machine learning projects, from design to production deployment, delivering high-impact solutions closely aligned with business needs.

✅ Model Development & Deployment

Designed, trained, and deployed multiple machine learning models into production, including a Cashout formula, a recommendation system, and a scoring model.

Ensured models were scalable, reliable, and integrated seamlessly into operational systems, generating tangible business value.

✅ Full-Stack Data Science

Covered the full project lifecycle: data collection, cleaning, feature engineering, model training, validation, and production deployment.

Built and maintained robust MLOps pipelines to guarantee automation, reproducibility, and monitoring of deployed models.

✅ Business-Centric Approach

Worked in close collaboration with business teams to align technical solutions with strategic objectives.

Focused on models that delivered direct ROI and measurable impact on decision-making processes.

✅ Leadership & Mentorship

Supervised and mentored interns, sharing best practices in data science, software engineering, and deployment.

Fostered a collaborative environment that improved code quality, efficiency, and knowledge transfer.

🧑‍💻 Data Scientist R&D – Predictive Modeling & Machine Learning

I supported business teams in unlocking the value of their data by developing and deploying predictive models into production, building robust data pipelines, and designing visualization tools that improved strategic decision-making.

✅ Advanced Modeling & Deployment

Designed, trained, and successfully deployed supervised (classification, regression) and unsupervised (clustering, label propagation) models into production environments.

Applied cutting-edge algorithms (LightGBM, Random Forest, scikit-learn).

Conducted predictive analyses, including employee absence forecasting, to anticipate operational risks.

✅ Data Engineering & ETL Pipelines

Built and optimized custom ETL pipelines to ensure data quality, integrity, and reliability.

Developed custom classes and integrated solutions into MLOps environments to automate workflows.

✅ Data Visualization & Interactive Tools

Created interactive dashboards (e.g., with Streamlit, Python) to make insights accessible to non-technical teams (marketing, HR, management).

✅ Model Explainability & Trust

Improved model transparency, making predictive systems understandable and actionable for stakeholders.

Translated complex outputs into clear, business-oriented recommendations.

✅ Collaboration & Mentorship

Mentored data science interns, sharing best practices in coding and modeling.

Worked closely with business teams to deliver data-driven solutions tailored to their needs.

🧑‍💻 Apprentice Data Scientist – Insurance Sector

I gained early-career experience in data analysis, machine learning, and model interpretability, contributing to projects that were both innovative and directly aligned with business needs.

✅ Data Analysis & Machine Learning

Conducted in-depth data analysis to uncover key business insights and opportunities.

Designed and trained machine learning models for various insurance use cases, with a focus on robustness and business relevance.

✅ Interpretability & Open-Source Contributions

Contributed to MAIF’s open-source projects, including Shapash and Eurybia, to enhance model transparency and explainability.

Delivered interpretable models that enabled stakeholders to understand and trust predictions.

✅ Production & Complex Feature Engineering

Developed and deployed a real estate price estimator in production, integrated with interpretability features for real-time predictions.

Tackled the complexity of understanding data and business context to create meaningful and robust features, crucial for model performance and reliability.